In recent years with most recorded music available in digital form, it became vastly accessible and convenient to average consumers to own or have access to large number of songs anytime anywhere. This in turn necessitates sorting and categorizing song files into collections, playlists, or mixtapes. Besides manual operations by users, many electronic devices and software programs, such as Apple's iTunes, Pandora, and Spotify, now existed to automatically sort and categorize a plurality of songs based on song attributes, user preferences, recommendations from group of multiple users, etc. Some of these electronic devices and software programs can also make automatic recommendations for inclusion of new songs in collections, playlists, or mixtapes by predicting the user's music tastes.
The most commonly used sorting and categorizing criteria include song attributes such as song artist, composer, release date, genre, country of origin; attributes on the user's listening habits such as play frequency, play count, and last play time and date; and attributes on listening habits of a community of users such as aggregated group rating on the song, popularity, and demographic of the user group. However, all of these criteria are based on extrinsic or predefined data of the song and nothing on the substance of the song itself. It is not known that any existing technology allows song sorting and categorizing by the acoustic features of the songs, which could enable sorting and categorizing criteria based on, for example, the mood (e.g. happy, sad, etc.) of the songs.